Yes, Machine Learning Can Be More Secure! A Case Study on Android
Malware Detection

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection

28 April 2017

Ambra Demontis

Battista Biggio

Giorgio Giacinto

Fabio Roli

Papers citing "Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection"

10 / 10 papers shown

Title
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems Ping He Lorenzo Cavallaro Shouling Ji AAML 127 0 0 23 Jan 2025
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations Dipo Dunsin M. C. Ghanem Karim Ouazzane Vassil T. Vassilev 43 0 0 19 Oct 2024
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version) Zeliang Kan Shae McFadden Daniel Arp Feargus Pendlebury Roberto Jordaney Johannes Kinder Fabio Pierazzi Lorenzo Cavallaro 51 1 0 02 Feb 2024
Adversarial Feature Selection against Evasion Attacks Fei Zhang P. Chan Battista Biggio D. Yeung Fabio Roli AAML 26 226 0 25 May 2020
Is feature selection secure against training data poisoning? Huang Xiao Battista Biggio Gavin Brown Giorgio Fumera Claudia Eckert Fabio Roli AAML SILM 41 423 0 21 Apr 2018
Security Evaluation of Pattern Classifiers under Attack Battista Biggio Giorgio Fumera Fabio Roli AAML 52 442 0 02 Sep 2017
On Security and Sparsity of Linear Classifiers for Adversarial Settings Ambra Demontis P. Russu Battista Biggio Giorgio Fumera Fabio Roli AAML 29 26 0 31 Aug 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 101 2,142 0 21 Aug 2017
Poisoning Attacks against Support Vector Machines Battista Biggio B. Nelson Pavel Laskov AAML 92 1,580 0 27 Jun 2012
Robustness and Regularization of Support Vector Machines Huan Xu Constantine Caramanis Shie Mannor 108 471 0 25 Mar 2008